Data Warehouse and Data Mining

Transcription

1 Data Warehouse and Data Mining Lecture No. 02 Lifecycle of Data warehouse Naeem Ahmed Department of Software Engineering Mehran Univeristy of Engineering and Technology Jamshoro

2 Outline Lifecycle of DW Classical SDLC vs. DW SDLC Operating DW Acknowledgements: Wolf-Tilo Balke and Silviu Homoceanu

3 Lifecycle of DW DW System Development Life Cycle (SDLC) Design End-user interview cycles Source system cataloging Definition of key performance indicators Mapping of decision-making processes underlying information needs Logical and physical schema design

4 Lifecycle of DW Prototype Objective is to constrain and in some cases reframe end-user requirements Deployment Development of documentation Training Operations and management processes Operation Day-to-day maintenance of the DW needs a good management of ongoing Extraction, Transformation and Loading (ETL) process

5 Lifecycle of DW Enhancement needs the modification of HW - physical components Operations and management processes Logical schema designs

6 Lifecycle of DW Classical SDLC vs. DW SDLC DW SDLC is almost the opposite of classical SDLC

7 Lifecycle of DW Classical SDLC vs. DW SDLC Because it is the opposite of SDLC, DW SDLC is also called CLDS

8 Lifecycle of DW CLDS is a data driven development life cycle It starts with data Once data is at hand it is integrated and tested against bias Programs are written against the data and the results are analyzed and finally the requirements of the system are understood Once requirements are understood, adjustments are made to the design and the cycle starts all over spiral development methodology

9 Operating a DW In Operating a DW the following phases can be identified Monitoring Extraction Transforming Loading Analyzing

10 Operating a DW: Monitoring Monitoring Surveillance of the data sources Identification of data modification which is relevant to the DW Monitoring has an important role over the whole process deciding on which data the next steps will be applied on Monitoring techniques Active mechanisms - Event Condition Action (ECA) rules:

11 Operating a DW: Monitoring Monitoring techniques Replication mechanisms Snapshot: Local copy of data, similar to a View Used by Oracle 9i Data replication Replicates and maintains data in destination tables through data propagation processes Used by IBM

12 Operating a DW: Monitoring Monitoring techniques Protocol based mechanisms Since DBMS write protocol data for transaction management, the protocol can be used also for monitoring Difficult due to the fact that the protocol format is proprietary and subject to change Application managed mechanisms Hard to implement for legacy systems Based on time stamping or data comparison

13 Operating a DW: Extraction Extraction Reads the data which was selected throughout the monitoring phase and inserts it in the data structures of the workplace Due to large data volume, compression can be used The time-point for performing extraction can be: Periodical: Weather or stock market information can be actualized more times in a day, while product specification can be actualized in a longer period of time On request: For example when a new item is added to a product group

14 Operating a DW: Extraction Extraction The time-point for performing extraction can be: Event driven: Event driven extraction can be helpful in scenarios where time, or the number of modifications over passing a specified threshold triggers the extraction. For example each night at 03:00 or each time 50 new modifications took place, an extraction is performed Immediate: In some special cases like the stock market it can be necessary that the changes propagate immediately to the warehouse The extraction largely depends on hardware and the software used for the DW and the data source

15 Operating a DW: Transforming Transforming Implies adapting data, schema as well as data quality to the application requirements Data integration: Transformation in de-normalized data structures Handling of key attributes Adaptation of different types of the same data Conversion of encoding: Buy, Sell 1,2 vs. B,S 1,2 Normalization: Michael Schumacher Michael, Schumacher vs. Schumacher Michael Michael, Schumacher

16 Operating a DW: Transforming Transforming Data integration: Date handling: MM-DD-YYYY MM.DD.YYYY Measurement units and scaling: 10 inch 25,4 cm 30 mph 48,279 km/h Save calculated values Price_incl_VAT = Price_excl_VAT * 1.19 Aggregation Daily sums can be added into weekly ones Different levels of granularity can be used

17 Transforming Data cleaning: Operating a DW: Transforming Consistency check Delivery_date < Order_date Completeness Management of missing values as well as NULL values

18 Operating a DW: Loading Loading Loading usually takes place during weekends or nights when the system is not under user stress Split between initial load to initialize the DW and the periodical load to keep the DW updated Initial loading Implies big volumes of data and for this reason a bulk loader is used Usually performed by partitioning, parallelization and incremental actualization

19 Analyze Data access Operating a DW: Analyzing Useful for extracting goal oriented information: How many iphones 3G were sold in the Braunschweig stores of T- Mobile in the last 3 calendar weeks of 2008? Although it is a common OLTP query, it might be to complex for the operational environment to handle OLAP Falsely used as representing DW because it is used to analyze data contained in DW Used to answer requests like: In which district does a product group register the highest profit How did the profit change in comparison to the previous month?

20 Analyze OLAP Operating a DW: Analyzing Used to answer requests like: Mostly known as organized on a multidimensional data model Common operations for analyze are: Data mining» Pivoting/Rotation» Roll-up, Drill-down and Drill-across» Slice and Dice Useful for identifying hidden patterns Refers to two separate processes: KDD (Knowledge Discovery in Databases) Prediction

21 Analyze Data mining Operating a DW: Analyzing Useful for answering questions like: How did the sales of this product group evolve? Methods and procedures for data mining Clustering, Classification, Regression, Association rule learning

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